Asymmetric Dependence Structures Research Articles

On the structure dynamic response induced by the dam-break surge impact using multivariate copulas

In this study, the dam-break induced surge impact pressure data on a vertical wall are used to investigate the corresponding structure dynamic response. Both the hydrodynamic and structure parameters are confirmed to be crucial in assessing the structure dynamic responses. The surge peak pressure pk, the pressure rise time Tr, the surge impact duration Td, and the structure natural period Tn are considered to establish the statistical model. The present study determines the dependencies among these parameters which describe the structure dynamic response due to the complexity of dam breaking. Accordingly, two multivariate copulas, i.e., the Gaussian copula and the Vine copula, are used to simulate the statistical properties of the maximum dynamic amplification factor ψm, allowing a better understanding of the dependencies and correlations among different parameters. The tail dependence of variable pairs (pk, Tr) and (pk, ψm) are not significant, while the variable pairs (Tr, ψm) and (Td/Tn, ψm) show lower and upper tail dependence. With respect to the experiment data, simulation using the Gaussian copula shows good results regarding variable pairs of symmetric dependence structures, whereas the Vine copula is more suitable for variable pairs of asymmetric dependence structures.

Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi

Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in understanding health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific class of generative, probabilistic graphical models that formally model asymmetric conditional independence and non-regular sample spaces. An investigation of the risk factors in invasive fungal infections demonstrates the insights staged trees provide to support medical decision-making.

Can China's national carbon trading market hedge the risks of light and medium crude oil? A comparative analysis with the European carbon market

The study explores whether China's national carbon market can hedge the risks of light and medium crude oil compared with the European carbon market, through the diversified and hedging portfolios estimated by the NAGARCHSK-Mixed Copula. The current work confirms the existence of conditional higher moments and asymmetric tail dependence structures in the crude and carbon markets. Furthermore, for all crude oil, although the China's carbon market is slightly inferior to the European carbon market in terms of hedging costs and stability, the hedging effectiveness of the former is better than that of the latter. And Oman crude oil gets better hedging protection from the carbon markets than Brent crude oil. Finally, the diversified portfolios obviously outperform the hedging portfolios in terms of hedging effectiveness.

Asymmetric dependence structures and decoupling hypothesis: Islamic versus conventional equity indices with copula approach

Purpose The purpose of this paper is to examine the dependence structure between the US conventional stock market and each Islamic and conventional stock market provided by the Dow Jones index, namely, for the UK, Canada, Europe, the emerging countries and Asia-Pacific. This paper considers both the bearish and bullish market phases of the 2008 global financial crisis to analyze the financial contagion. Design/methodology/approach The authors implement the copula framework-based GJR-GARCH-t model for the period from December 31, 2004 to September 30, 2016. Findings The marginal models suggest a strong persistence of volatility in all stock markets. The dependence structure for stock market pairs under-consideration is not all strictly symmetrical. Moreover, the Islamic stock markets witness the same behavior as their conventional counterparts. Finally, the resilience and the decoupling hypotheses are not all around upheld by the empirical proof. Originality/value The findings of this paper are very important for global investors in their risk management during extreme market events. As the Sukuk is considered as a safe haven during crisis episodes, the investors are invited to take it into account for further portfolio diversification.

Price extremes and asymmetric dependence structures in stock returns: the emerging market evidence

PurposeEquity research in experimental psychology reveals investors' overreactions to bad news events. This study of asymmetric price structures in equity markets investigates whether such behavior predicts stock returns in an emerging market of India.Design/methodology/approachThe research decomposes Bombay Stock Exchange (BSE) Sensex returns into Extremely Positive Returns (EPR) and Extremely Negative Returns (ENR) based on extreme values at first and then tests their lead–lag relations.FindingsThe empirical finding is consistent with the existing evidence of asymmetric news effects on stock returns in India. In precise, ENR robustly predicts one-month-ahead EPR for the sample period from January 1991 to March 2020. This predictive power persists even in the presence of popular valuation ratios and business cycle variables.Practical implicationsThe paper explains the rationale of extreme value modeling in price forecasting. Investors can find additional utility gains from market cycle information while predicting extreme returns in Indian stock market.Originality/valueThe paper is unique to understand business cycle effects in extreme return reversals in emerging markets.

Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations

It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework that incorporates such asymmetric dependency structures using a nonparametric measure of local dependence, the local Gaussian correlation (LGC). It is shown that the portfolio optimization process for financial returns with asymmetric dependence structures is straightforward using local covariance matrices. The new method is shown to outperform the equally weighted (“1/N”) portfolio and the classical Markowitz portfolio when applied to historical data on six assets.

Condition-based maintenance strategies for stochastically dependent systems using Nested Lévy copulas

Nowadays, industrial systems are more and more complex and components are integrated dependently. Without consideration of dependence between components often leads to overestimate the reliability of system and high maintenance cost. In this paper, a multi-component system with hierarchical stochastic dependencies is considered. Components within subsystem are strongly correlated and dependence between subsystems is heterogeneous. Thanks to its capacity of modeling asymmetric dependence structures, the Nested Lévy copula is used in this paper. Based on the multiple sources of dependence among components and hierarchical structures, a novel condition-based maintenance policy is proposed. The inspection scheme as well as maintenance decision is dynamically planned according to the collected inspection information and the influence of stochastic dependence is investigated. The numerical results show the efficiency and robustness of proposed maintenance policy on reducing the inspection and maintenance cost.

Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach

AbstractWe propose a copula‐based periodic mixed frequency generalized autoregressive (GAS) framework in order to model and forecast the intraday exposure conditional value at risk (ECoVaR) for an intraday asset return and the corresponding market return. In particular, we analyze GAS models that account for long‐memory‐type of dependencies, periodicities, asymmetric nonlinear dependence structures, fat‐tailed conditional return distributions, and intraday jump processes for asset returns. We apply our framework in order to analyze the ECoVaR forecasting performance for a large data set of intraday asset returns of the S&P500 index.

Asymmetric dependence structures between emission allowances and energy markets: new evidence from China's emissions trading scheme pilots.

This article investigates the dependence structure between China's emissions allowances and energy (coal, oil, and natural gas) markets using autoregressive conditional heteroskedasticity (GARCH) and mixed Copula functions. The dependence structures between China's eight emissions allowances and energy markets demonstrate significant regional heterogeneity; an increase (decrease) in coal, crude oil, and natural gas prices induces an increase (decrease) in eight regional emissions allowance prices and confirms regional divergence and market heterogeneity. The Beijing, Guangdong, Hubei, and Chongqing emissions allowances and coal markets demonstrate significant left-tail dependence structures, while the Shanghai, Tianjin, Shenzhen, and Fujian emissions allowances and coal markets demonstrate complex mixed dependence structures. Seven of China's emissions allowances and crude oil markets confirm the existence of complex asymmetric dependence structures, except for the symmetric dependence structure between the Fujian emissions allowances and crude oil markets. However, eight of China's emissions allowances and natural gas markets demonstrate complex asymmetric dependence structures.

Asymmetric dependence structures for regional stock markets: An unconditional quantile regression approach

Owing to the asymmetry of stock markets, this study investigates the dependence structures for six regional stock markets according to different market conditions by applying the unconditional quantile regression (UQR) approach. This approach can address the traditional conditional quantile regression (CQR) approach’s limitation that its distributions are defined conditional on specific covariates. Specifically, we not only examine the detailed linkages among these six regional stock markets, but also explore the effect of global economic factors on them, given the strengthening of both international investment and the globalization of financial markets. The results show these dependence structures are often an asymmetric U-shaped or inverted U-shaped structure, which indicates that the impacts of both other geographically and economically close stock markets and economic factors are more pronounced during bear and bull markets than during normal markets, especially so in bear markets. Moreover, the UQR approach provides stronger extreme-value relationships and more significant asymmetric effects than the traditional CQR approach.

Modeling asymmetric dependences among multivariate soil data for the geotechnical analysis – The asymmetric copula approach

Multivariate information of soil parameters is quite important for the design and risk assessment of geotechnical engineering problems. It is necessary to have an accurate and realistic statistical multivariate model for representing the soil properties and thus evaluating the soil conditions. Thus, advanced multivariate modeling of soil parameters could help to improve the geotechnical engineering practice. In this paper, the asymmetric copulas are introduced to model the geotechnical soil data. Compared to extensive previous research on the use of symmetric copulas on the modeling of engineering data, this study is focusing on capturing asymmetric dependencies among the natural soil parameters, which are critical for engineering design. A copula-based multivariate probabilistic model is built based on a set of collected samples from a granite residual soil from Portugal. Several asymmetric copula functions, capable of capturing nonlinear asymmetric dependence structures, are tested and analyzed. The fundamental information on tail dependencies and measures of asymmetric dependencies are also exploited. To demonstrate the advantages of asymmetric copulas, its concept is compared with the traditional copula approaches for modeling site soil data. The performance of these asymmetric copulas is discussed and compared based on data fitting and extreme value characterizations.

Intraday Conditional Value at Risk: A Periodic Mixed-Frequency GAS Approach

We propose a copula-based periodic mixed frequency GAS framework in order to model and forecast the intraday Exposure Conditional Value at Risk (ECoVaR) for an intraday asset return and the corresponding market return. In particular we analyze GAS models which account for long-memory-type of dependencies, periodicities, asymmetric nonlinear dependence structures, fat-tailed conditional return distributions and intraday jump processes for asset returns. We apply our framework in order to analyze the in-sample and out-of-sample ECoVaR forecasting performance for a large data set of intraday asset returns of the S&P500 index.

Modeling multivariate ocean data using asymmetric copulas

Multivariate descriptions of ocean parameters are quite important for the design and risk assessment of offshore engineering applications. A reliable and realistic statistical multivariate model is essential to produce a representative estimate of the sea state for understanding the ocean conditions. Therefore, an advanced modeling of ocean parameters helps towards improving ocean and coastal engineering practices. In this paper, we introduce the concepts of asymmetric copulas for the modeling of multivariate ocean data. In contrast to extensive previous research on the modeling of symmetric ocean data, this study is focused on capturing asymmetric dependencies among the environmental parameters, which are critical for a realistic description of ocean conditions. This involves particular attention to both nonlinear and asymmetrically dependent variates, which are quite common for the ocean variables. Several asymmetric copula functions, capable of modeling both linear and nonlinear asymmetric dependence structures, are examined in detail. Information on tail dependencies and measures of asymmetric dependencies are exploited. To demonstrate the advantages of asymmetric copulas, the asymmetric copula concept is compared with the traditional copula approaches from the literature using actual environmental data. Each of the introduced copula models is fitted to a set of ocean data collected from a buoy at the US coast. The performance of these asymmetric copulas is discussed and compared based on data fitting and tail dependency characterizations. The accuracy of asymmetric copulas in predicting the extreme value contours is discussed.

Oil Price Systemic Risk for the Oil and Gas Industry: A Copula-CoVaR Approach

This paper contributes to the literature on the relationship between crude oil prices and financial markets. Copula models are used in order to investigate the time-varying and asymmetric dependence structure between oil prices and stock prices of oil and gas companies. In addition, the concept of conditional value- at-risk (CoVaR) is used in order to quantify the oil price systemic risk for the oil and gas sector companies in different countries. The approaches applied in this paper are very flexible and allow one to capture dynamic, non-linear, in particular asymmetric, relationships. This seems reasonable as oil price behaviour as such, the underlying causes for price movements as well as the way financial markets react to oil price movements changes over time. A newly proposed marginal model is found to outperform traditional approaches. In addition, there is evidence of time-varying and asymmetric dependence structures. Finally, both upside and downside oil price risk are found to be significant. Thus, our results show the different impacts of oil prices on the stock prices of oil and gas companies in different countries which allows investors of oil and gas industry to construct global diversified portfolios to hedge the oil price systemic risk optimally. In addition, governments in different countries can make policy decisions for adjusting their international oil trade strategy to optimize the revenues of domestic oil and gas companies in order to optimising tax revenues.

A comparison of dependence function estimators in multivariate extremes

Various nonparametric and parametric estimators of extremal dependence have been proposed in the literature. Nonparametric methods commonly suffer from the curse of dimensionality and have been mostly implemented in extreme-value studies up to three dimensions, whereas parametric models can tackle higher-dimensional settings. In this paper, we assess, through a vast and systematic simulation study, the performance of classical and recently proposed estimators in multivariate settings. In particular, we first investigate the performance of nonparametric methods and then compare them with classical parametric approaches under symmetric and asymmetric dependence structures within the commonly used logistic family. We also explore two different ways to make nonparametric estimators satisfy the necessary dependence function shape constraints, finding a general improvement in estimator performance either (i) by substituting the estimator with its greatest convex minorant, developing a computational tool to implement this method for dimensions $$D\ge 2$$ or (ii) by projecting the estimator onto a subspace of dependence functions satisfying such constraints and taking advantage of Bernstein–Bezier polynomials. Implementing the convex minorant method leads to better estimator performance as the dimensionality increases.

An order of asymmetry in copulas, and implications for risk management

We study symmetry properties of bivariate copulas. For this, we introduce an order of asymmetry, as well as measures of asymmetry which are monotone in that order. In an empirical study, we illustrate that asymmetric dependence structures do indeed occur in financial market data and discuss its relevance for financial risk management.

Modeling multi-country mortality dependence and its application in pricing survivor index swaps—A dynamic copula approach

This paper introduces mortality dependence in multi-country mortality modeling using a dynamic copula approach. Specifically, we use time-varying copula models to capture the mortality dependence structure across countries, examining both symmetric and asymmetric dependence structures. In addition, to capture the phenomenon of a heavy tail for the multi-country mortality index, we consider not only the setting of Gaussian innovations but also non-Gaussian innovations under the Lee–Carter framework model. As tests of the goodness of fit of different dynamic copula models, the pattern of mortality dependence, and the distribution of the innovations, we used empirical mortality data from Finland, France, the Netherlands, and Sweden. To understand the effect of mortality dependence on longevity derivatives, we also built a valuation framework for pricing a survivor index swap, then investigated the fair swap rates of a survivor swap numerically. We demonstrate that failing to consider the dynamic copula mortality model and non-Gaussian innovations would lead to serious underestimations of the swap rates and loss reserves.

Chain Event Graphs for Informed Missingness

Chain Event Graphs (CEGs) are proving to be a useful framework for modelling discrete processes which exhibit strong asymmetric dependence structures between the variables of the problem. In this paper we exploit this framework to represent processes where missingness is influential and data cannot plausibly be hypothesised to be missing at random in all situations. We develop new classes of models where data are missing not at random but nevertheless exhibit context-specific symmetries which are captured by the CEG. We show that it is possible to score each model efficiently and in closed form. Hence standard Bayesian selection methods can be used to search over a wide variety of models, each with its own explanatory narrative. One of the advantages of this method is that the selected maximum a posteriori model and other closely scoring models can be easily read back to the client in a graphically transparent way. The efficacy of our methods are illustrated using a cerebral palsy cohort study, analysing their survival with respect to weight at birth and various disabilities.

Call-type dependence in multiskill call centers

The effect on multiskill call-center performance of pooling dependent call types is investigated. For this purpose, a copula-based modeling approach is used to provide multivariate models that take into account the call types’ asymmetric dependence structures found in empirical data. Then, the realistic input models of the call-type-dependent arrival processes are used in a simulation study to explore the sensitivity of the pooling decision to this dependence. We find that the widely used assumption of independence, as well as the misspecification of the dependence structure, can lead to substantial misestimation of call-center performance. This demonstrates the importance of modeling call-type dependence in stochastic simulation studies of call centers. We also show, through case studies, that pooling two asymmetric left-tail-dependent call types is more likely to lead to low agents occupancy; whereas the presence of right-tail dependence structure increases the risk of service-level shortfall. This work provides new managerial insights to improve decision making in determining which call types to merge in the same pool in multiskill call centers.

Canonical vine copulas in the context of modern portfolio management: Are they worth it?

In the context of managing downside correlations, we examine the use of multi-dimensional elliptical and asymmetric copula models to forecast returns for portfolios with 3–12 constituents. Our analysis assumes that investors have no short-sales constraints and a utility function characterized by the minimization of Conditional Value-at-Risk (CVaR). We examine the efficient frontiers produced by each model and focus on comparing two methods for incorporating scalable asymmetric dependence structures across asset returns using the Archimedean Clayton copula in an out-of-sample, long-run multi-period setting. For portfolios of higher dimensions, we find that modeling asymmetries within the marginals and the dependence structure with the Clayton canonical vine copula (CVC) consistently produces the highest-ranked outcomes across a range of statistical and economic metrics when compared to other models incorporating elliptical or symmetric dependence structures. Accordingly, we conclude that CVC copulas are ‘worth it’ when managing larger portfolios.